Overview

Brought to you by YData

Dataset statistics

Number of variables31
Number of observations899164
Missing cells746666
Missing cells (%)2.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory212.7 MiB
Average record size in memory248.0 B

Variable types

Numeric12
Text9
DateTime3
Categorical6
Boolean1

Alerts

ApprovalFY is highly overall correlated with RetainedJob and 1 other fieldsHigh correlation
LoanNr_ChkDgt is highly overall correlated with crisisHigh correlation
NAICS is highly overall correlated with cat_activitesHigh correlation
RetainedJob is highly overall correlated with ApprovalFYHigh correlation
SBA_loan_float is highly overall correlated with Term and 1 other fieldsHigh correlation
Term is highly overall correlated with SBA_loan_float and 1 other fieldsHigh correlation
UrbanRural is highly overall correlated with ApprovalFYHigh correlation
bank_loan_float is highly overall correlated with SBA_loan_float and 1 other fieldsHigh correlation
cat_activites is highly overall correlated with NAICSHigh correlation
crisis is highly overall correlated with LoanNr_ChkDgtHigh correlation
RevLineCr is highly imbalanced (61.3%) Imbalance
BalanceGross is highly imbalanced (> 99.9%) Imbalance
ChgOffDate has 736465 (81.9%) missing values Missing
NoEmp is highly skewed (γ1 = 80.24824355) Skewed
CreateJob is highly skewed (γ1 = 36.99135473) Skewed
RetainedJob is highly skewed (γ1 = 36.85481184) Skewed
LoanNr_ChkDgt has unique values Unique
NAICS has 201948 (22.5%) zeros Zeros
CreateJob has 629248 (70.0%) zeros Zeros
RetainedJob has 440403 (49.0%) zeros Zeros
FranchiseCode has 208835 (23.2%) zeros Zeros
cat_activites has 201948 (22.5%) zeros Zeros

Reproduction

Analysis started2025-02-06 15:21:18.479688
Analysis finished2025-02-06 15:23:19.989211
Duration2 minutes and 1.51 second
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

LoanNr_ChkDgt
Real number (ℝ)

High correlation  Unique 

Distinct899164
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7726123 × 109
Minimum1.000014 × 109
Maximum9.996003 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2025-02-06T16:23:20.329090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.000014 × 109
5-th percentile1.3484572 × 109
Q12.5897575 × 109
median4.361439 × 109
Q36.9046265 × 109
95-th percentile9.1648039 × 109
Maximum9.996003 × 109
Range8.995989 × 109
Interquartile range (IQR)4.314869 × 109

Descriptive statistics

Standard deviation2.538175 × 109
Coefficient of variation (CV)0.53182091
Kurtosis-1.086499
Mean4.7726123 × 109
Median Absolute Deviation (MAD)2.0134 × 109
Skewness0.3647571
Sum4.2913612 × 1015
Variance6.4423325 × 1018
MonotonicityStrictly increasing
2025-02-06T16:23:20.423140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9996003010 1
 
< 0.1%
1000014003 1
 
< 0.1%
1000024006 1
 
< 0.1%
1000034009 1
 
< 0.1%
1000044001 1
 
< 0.1%
1000054004 1
 
< 0.1%
1000084002 1
 
< 0.1%
1000093009 1
 
< 0.1%
1000094005 1
 
< 0.1%
1000104006 1
 
< 0.1%
Other values (899154) 899154
> 99.9%
ValueCountFrequency (%)
1000014003 1
< 0.1%
1000024006 1
< 0.1%
1000034009 1
< 0.1%
1000044001 1
< 0.1%
1000054004 1
< 0.1%
1000084002 1
< 0.1%
1000093009 1
< 0.1%
1000094005 1
< 0.1%
1000104006 1
< 0.1%
1000124001 1
< 0.1%
ValueCountFrequency (%)
9996003010 1
< 0.1%
9995973006 1
< 0.1%
9995613003 1
< 0.1%
9995603000 1
< 0.1%
9995573004 1
< 0.1%
9995563001 1
< 0.1%
9995493004 1
< 0.1%
9995473009 1
< 0.1%
9995453003 1
< 0.1%
9995423005 1
< 0.1%

Name
Text

Distinct779583
Distinct (%)86.7%
Missing14
Missing (%)< 0.1%
Memory size6.9 MiB
2025-02-06T16:23:20.909523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length30
Median length23
Mean length21.775963
Min length1

Characters and Unicode

Total characters19579857
Distinct characters91
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique706468 ?
Unique (%)78.6%

Sample

1st rowABC HOBBYCRAFT
2nd rowLANDMARK BAR & GRILLE (THE)
3rd rowWHITLOCK DDS, TODD M.
4th rowBIG BUCKS PAWN & JEWELRY, LLC
5th rowANASTASIA CONFECTIONS, INC.
ValueCountFrequency (%)
inc 263379
 
8.4%
100280
 
3.2%
llc 77826
 
2.5%
and 28959
 
0.9%
the 28389
 
0.9%
of 23026
 
0.7%
dba 20214
 
0.6%
co 18216
 
0.6%
a 18114
 
0.6%
services 17318
 
0.6%
Other values (226643) 2530176
80.9%
2025-02-06T16:23:21.468796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2231639
 
11.4%
E 1354056
 
6.9%
I 1226719
 
6.3%
A 1177821
 
6.0%
N 1170319
 
6.0%
R 1052562
 
5.4%
C 1038114
 
5.3%
S 1009495
 
5.2%
O 933206
 
4.8%
T 917437
 
4.7%
Other values (81) 7468489
38.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19579857
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2231639
 
11.4%
E 1354056
 
6.9%
I 1226719
 
6.3%
A 1177821
 
6.0%
N 1170319
 
6.0%
R 1052562
 
5.4%
C 1038114
 
5.3%
S 1009495
 
5.2%
O 933206
 
4.8%
T 917437
 
4.7%
Other values (81) 7468489
38.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19579857
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2231639
 
11.4%
E 1354056
 
6.9%
I 1226719
 
6.3%
A 1177821
 
6.0%
N 1170319
 
6.0%
R 1052562
 
5.4%
C 1038114
 
5.3%
S 1009495
 
5.2%
O 933206
 
4.8%
T 917437
 
4.7%
Other values (81) 7468489
38.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19579857
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2231639
 
11.4%
E 1354056
 
6.9%
I 1226719
 
6.3%
A 1177821
 
6.0%
N 1170319
 
6.0%
R 1052562
 
5.4%
C 1038114
 
5.3%
S 1009495
 
5.2%
O 933206
 
4.8%
T 917437
 
4.7%
Other values (81) 7468489
38.1%

City
Text

Distinct32581
Distinct (%)3.6%
Missing30
Missing (%)< 0.1%
Memory size6.9 MiB
2025-02-06T16:23:21.695508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length30
Median length27
Mean length9.1030625
Min length1

Characters and Unicode

Total characters8184873
Distinct characters80
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12872 ?
Unique (%)1.4%

Sample

1st rowEVANSVILLE
2nd rowNEW PARIS
3rd rowBLOOMINGTON
4th rowBROKEN ARROW
5th rowORLANDO
ValueCountFrequency (%)
city 23831
 
2.0%
san 21942
 
1.8%
new 16075
 
1.3%
los 13000
 
1.1%
angeles 12380
 
1.0%
lake 10729
 
0.9%
houston 10587
 
0.9%
beach 10462
 
0.9%
park 10316
 
0.9%
york 9724
 
0.8%
Other values (17695) 1066583
88.5%
2025-02-06T16:23:22.020774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 744405
 
9.1%
E 723098
 
8.8%
O 632510
 
7.7%
N 621338
 
7.6%
L 573578
 
7.0%
R 513614
 
6.3%
S 475392
 
5.8%
I 468344
 
5.7%
T 425108
 
5.2%
306936
 
3.8%
Other values (70) 2700550
33.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8184873
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 744405
 
9.1%
E 723098
 
8.8%
O 632510
 
7.7%
N 621338
 
7.6%
L 573578
 
7.0%
R 513614
 
6.3%
S 475392
 
5.8%
I 468344
 
5.7%
T 425108
 
5.2%
306936
 
3.8%
Other values (70) 2700550
33.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8184873
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 744405
 
9.1%
E 723098
 
8.8%
O 632510
 
7.7%
N 621338
 
7.6%
L 573578
 
7.0%
R 513614
 
6.3%
S 475392
 
5.8%
I 468344
 
5.7%
T 425108
 
5.2%
306936
 
3.8%
Other values (70) 2700550
33.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8184873
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 744405
 
9.1%
E 723098
 
8.8%
O 632510
 
7.7%
N 621338
 
7.6%
L 573578
 
7.0%
R 513614
 
6.3%
S 475392
 
5.8%
I 468344
 
5.7%
T 425108
 
5.2%
306936
 
3.8%
Other values (70) 2700550
33.0%

State
Text

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
2025-02-06T16:23:22.142868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1798328
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowIN
2nd rowIN
3rd rowIN
4th rowOK
5th rowFL
ValueCountFrequency (%)
ca 130620
 
14.5%
tx 70463
 
7.8%
ny 57693
 
6.4%
fl 41213
 
4.6%
pa 35170
 
3.9%
oh 32622
 
3.6%
il 29669
 
3.3%
ma 25272
 
2.8%
mn 24373
 
2.7%
nj 24036
 
2.7%
Other values (42) 428033
47.6%
2025-02-06T16:23:22.324772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 306178
17.0%
C 184958
10.3%
N 181728
10.1%
M 132550
 
7.4%
T 125075
 
7.0%
I 119520
 
6.6%
O 94907
 
5.3%
L 88820
 
4.9%
X 70463
 
3.9%
Y 68255
 
3.8%
Other values (14) 425874
23.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1798328
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 306178
17.0%
C 184958
10.3%
N 181728
10.1%
M 132550
 
7.4%
T 125075
 
7.0%
I 119520
 
6.6%
O 94907
 
5.3%
L 88820
 
4.9%
X 70463
 
3.9%
Y 68255
 
3.8%
Other values (14) 425874
23.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1798328
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 306178
17.0%
C 184958
10.3%
N 181728
10.1%
M 132550
 
7.4%
T 125075
 
7.0%
I 119520
 
6.6%
O 94907
 
5.3%
L 88820
 
4.9%
X 70463
 
3.9%
Y 68255
 
3.8%
Other values (14) 425874
23.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1798328
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 306178
17.0%
C 184958
10.3%
N 181728
10.1%
M 132550
 
7.4%
T 125075
 
7.0%
I 119520
 
6.6%
O 94907
 
5.3%
L 88820
 
4.9%
X 70463
 
3.9%
Y 68255
 
3.8%
Other values (14) 425874
23.7%

Zip
Real number (ℝ)

Distinct33611
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53804.391
Minimum0
Maximum99999
Zeros283
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2025-02-06T16:23:22.410999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3838
Q127587
median55410
Q383704
95-th percentile95822
Maximum99999
Range99999
Interquartile range (IQR)56117

Descriptive statistics

Standard deviation31184.159
Coefficient of variation (CV)0.5795839
Kurtosis-1.3359893
Mean53804.391
Median Absolute Deviation (MAD)28206
Skewness-0.16816663
Sum4.8378972 × 1010
Variance9.7245178 × 108
MonotonicityNot monotonic
2025-02-06T16:23:22.504595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10001 933
 
0.1%
90015 926
 
0.1%
93401 806
 
0.1%
90010 733
 
0.1%
33166 671
 
0.1%
90021 666
 
0.1%
59601 640
 
0.1%
65804 599
 
0.1%
3801 581
 
0.1%
59101 578
 
0.1%
Other values (33601) 892031
99.2%
ValueCountFrequency (%)
0 283
< 0.1%
1 24
 
< 0.1%
2 11
 
< 0.1%
3 5
 
< 0.1%
4 5
 
< 0.1%
5 5
 
< 0.1%
6 4
 
< 0.1%
7 6
 
< 0.1%
8 15
 
< 0.1%
9 24
 
< 0.1%
ValueCountFrequency (%)
99999 209
< 0.1%
99950 3
 
< 0.1%
99929 15
 
< 0.1%
99928 1
 
< 0.1%
99926 1
 
< 0.1%
99925 4
 
< 0.1%
99923 1
 
< 0.1%
99921 13
 
< 0.1%
99919 2
 
< 0.1%
99918 1
 
< 0.1%

Bank
Text

Distinct5802
Distinct (%)0.6%
Missing1559
Missing (%)0.2%
Memory size6.9 MiB
2025-02-06T16:23:22.693655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length30
Median length26
Mean length23.187946
Min length3

Characters and Unicode

Total characters20813616
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique923 ?
Unique (%)0.1%

Sample

1st rowFIFTH THIRD BANK
2nd row1ST SOURCE BANK
3rd rowGRANT COUNTY STATE BANK
4th row1ST NATL BK & TR CO OF BROKEN
5th rowFLORIDA BUS. DEVEL CORP
ValueCountFrequency (%)
bank 651608
18.5%
natl 318240
 
9.0%
assoc 306768
 
8.7%
of 142852
 
4.1%
national 125899
 
3.6%
america 100686
 
2.9%
association 84965
 
2.4%
fargo 63732
 
1.8%
wells 63650
 
1.8%
52264
 
1.5%
Other values (3602) 1606709
45.7%
2025-02-06T16:23:22.972097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 2762231
13.3%
2620014
12.6%
N 2105500
10.1%
S 1520499
 
7.3%
O 1336993
 
6.4%
T 1181841
 
5.7%
C 1134642
 
5.5%
I 1061717
 
5.1%
E 923739
 
4.4%
L 922583
 
4.4%
Other values (40) 5243857
25.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20813616
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 2762231
13.3%
2620014
12.6%
N 2105500
10.1%
S 1520499
 
7.3%
O 1336993
 
6.4%
T 1181841
 
5.7%
C 1134642
 
5.5%
I 1061717
 
5.1%
E 923739
 
4.4%
L 922583
 
4.4%
Other values (40) 5243857
25.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20813616
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 2762231
13.3%
2620014
12.6%
N 2105500
10.1%
S 1520499
 
7.3%
O 1336993
 
6.4%
T 1181841
 
5.7%
C 1134642
 
5.5%
I 1061717
 
5.1%
E 923739
 
4.4%
L 922583
 
4.4%
Other values (40) 5243857
25.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20813616
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 2762231
13.3%
2620014
12.6%
N 2105500
10.1%
S 1520499
 
7.3%
O 1336993
 
6.4%
T 1181841
 
5.7%
C 1134642
 
5.5%
I 1061717
 
5.1%
E 923739
 
4.4%
L 922583
 
4.4%
Other values (40) 5243857
25.2%
Distinct56
Distinct (%)< 0.1%
Missing1566
Missing (%)0.2%
Memory size6.9 MiB
2025-02-06T16:23:23.089986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1795196
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowOH
2nd rowIN
3rd rowIN
4th rowOK
5th rowFL
ValueCountFrequency (%)
ca 118116
 
13.2%
nc 79514
 
8.9%
il 65908
 
7.3%
oh 58461
 
6.5%
sd 51095
 
5.7%
tx 47790
 
5.3%
ri 45366
 
5.1%
ny 39592
 
4.4%
va 29002
 
3.2%
de 24537
 
2.7%
Other values (46) 338217
37.7%
2025-02-06T16:23:23.266079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 241398
13.4%
C 229604
12.8%
N 187751
10.5%
I 158854
 
8.8%
O 102604
 
5.7%
L 96914
 
5.4%
D 96078
 
5.4%
T 94941
 
5.3%
M 85034
 
4.7%
S 73385
 
4.1%
Other values (14) 428633
23.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1795196
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 241398
13.4%
C 229604
12.8%
N 187751
10.5%
I 158854
 
8.8%
O 102604
 
5.7%
L 96914
 
5.4%
D 96078
 
5.4%
T 94941
 
5.3%
M 85034
 
4.7%
S 73385
 
4.1%
Other values (14) 428633
23.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1795196
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 241398
13.4%
C 229604
12.8%
N 187751
10.5%
I 158854
 
8.8%
O 102604
 
5.7%
L 96914
 
5.4%
D 96078
 
5.4%
T 94941
 
5.3%
M 85034
 
4.7%
S 73385
 
4.1%
Other values (14) 428633
23.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1795196
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 241398
13.4%
C 229604
12.8%
N 187751
10.5%
I 158854
 
8.8%
O 102604
 
5.7%
L 96914
 
5.4%
D 96078
 
5.4%
T 94941
 
5.3%
M 85034
 
4.7%
S 73385
 
4.1%
Other values (14) 428633
23.9%

NAICS
Real number (ℝ)

High correlation  Zeros 

Distinct1312
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean398660.95
Minimum0
Maximum928120
Zeros201948
Zeros (%)22.5%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2025-02-06T16:23:23.345949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1235210
median445310
Q3561730
95-th percentile811192
Maximum928120
Range928120
Interquartile range (IQR)326520

Descriptive statistics

Standard deviation263318.31
Coefficient of variation (CV)0.66050691
Kurtosis-1.0476526
Mean398660.95
Median Absolute Deviation (MAD)176300
Skewness-0.26287834
Sum3.5846157 × 1011
Variance6.9336534 × 1010
MonotonicityNot monotonic
2025-02-06T16:23:23.436159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 201948
 
22.5%
722110 27989
 
3.1%
722211 19448
 
2.2%
811111 14585
 
1.6%
621210 14048
 
1.6%
624410 10111
 
1.1%
812112 9230
 
1.0%
561730 8935
 
1.0%
621310 8733
 
1.0%
812320 7894
 
0.9%
Other values (1302) 576243
64.1%
ValueCountFrequency (%)
0 201948
22.5%
111110 32
 
< 0.1%
111120 3
 
< 0.1%
111130 1
 
< 0.1%
111140 94
 
< 0.1%
111150 49
 
< 0.1%
111160 2
 
< 0.1%
111191 3
 
< 0.1%
111199 7
 
< 0.1%
111211 16
 
< 0.1%
ValueCountFrequency (%)
928120 32
< 0.1%
928110 4
 
< 0.1%
927110 1
 
< 0.1%
926150 10
 
< 0.1%
926140 6
 
< 0.1%
926130 3
 
< 0.1%
926120 5
 
< 0.1%
926110 6
 
< 0.1%
925120 1
 
< 0.1%
925110 3
 
< 0.1%
Distinct9859
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
Minimum1961-12-07 00:00:00
Maximum2014-06-25 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-06T16:23:23.526133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:23.617495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ApprovalFY
Real number (ℝ)

High correlation 

Distinct51
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2001.1436
Minimum1962
Maximum2014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2025-02-06T16:23:23.715492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1962
5-th percentile1991
Q11997
median2002
Q32006
95-th percentile2009
Maximum2014
Range52
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.9138459
Coefficient of variation (CV)0.0029552332
Kurtosis-0.092531047
Mean2001.1436
Median Absolute Deviation (MAD)4
Skewness-0.58537855
Sum1.7993562 × 109
Variance34.973573
MonotonicityNot monotonic
2025-02-06T16:23:23.808570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2005 77525
 
8.6%
2006 76040
 
8.5%
2007 71876
 
8.0%
2004 68290
 
7.6%
2003 58193
 
6.5%
1995 45758
 
5.1%
2002 44391
 
4.9%
1996 40112
 
4.5%
2008 39540
 
4.4%
1997 37748
 
4.2%
Other values (41) 339691
37.8%
ValueCountFrequency (%)
1962 1
 
< 0.1%
1965 1
 
< 0.1%
1966 1
 
< 0.1%
1967 2
 
< 0.1%
1968 2
 
< 0.1%
1969 4
 
< 0.1%
1970 8
 
< 0.1%
1971 20
 
< 0.1%
1972 27
< 0.1%
1973 52
< 0.1%
ValueCountFrequency (%)
2014 268
 
< 0.1%
2013 2458
 
0.3%
2012 5997
 
0.7%
2011 12608
 
1.4%
2010 16848
 
1.9%
2009 19126
 
2.1%
2008 39540
4.4%
2007 71876
8.0%
2006 76040
8.5%
2005 77525
8.6%

Term
Real number (ℝ)

High correlation 

Distinct412
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110.77308
Minimum0
Maximum569
Zeros810
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2025-02-06T16:23:23.893323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16
Q160
median84
Q3120
95-th percentile300
Maximum569
Range569
Interquartile range (IQR)60

Descriptive statistics

Standard deviation78.857305
Coefficient of variation (CV)0.7118815
Kurtosis0.18570424
Mean110.77308
Median Absolute Deviation (MAD)33
Skewness1.1209258
Sum99603164
Variance6218.4746
MonotonicityNot monotonic
2025-02-06T16:23:23.979111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84 230162
25.6%
60 89945
 
10.0%
240 85982
 
9.6%
120 77654
 
8.6%
300 44727
 
5.0%
180 28164
 
3.1%
36 19800
 
2.2%
12 17095
 
1.9%
48 15621
 
1.7%
72 9419
 
1.0%
Other values (402) 280595
31.2%
ValueCountFrequency (%)
0 810
 
0.1%
1 1608
0.2%
2 1809
0.2%
3 2112
0.2%
4 2173
0.2%
5 1866
0.2%
6 3054
0.3%
7 1761
0.2%
8 1693
0.2%
9 1875
0.2%
ValueCountFrequency (%)
569 1
< 0.1%
527 1
< 0.1%
511 1
< 0.1%
505 1
< 0.1%
481 1
< 0.1%
480 1
< 0.1%
461 1
< 0.1%
449 1
< 0.1%
445 1
< 0.1%
443 1
< 0.1%

NoEmp
Real number (ℝ)

Skewed 

Distinct599
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.411353
Minimum0
Maximum9999
Zeros6631
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2025-02-06T16:23:24.068887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q310
95-th percentile40
Maximum9999
Range9999
Interquartile range (IQR)8

Descriptive statistics

Standard deviation74.108196
Coefficient of variation (CV)6.4942514
Kurtosis7965.2886
Mean11.411353
Median Absolute Deviation (MAD)3
Skewness80.248244
Sum10260678
Variance5492.0248
MonotonicityNot monotonic
2025-02-06T16:23:24.158468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 154254
17.2%
2 138297
15.4%
3 90674
10.1%
4 73644
 
8.2%
5 60319
 
6.7%
6 45759
 
5.1%
10 31536
 
3.5%
7 31495
 
3.5%
8 31361
 
3.5%
12 20822
 
2.3%
Other values (589) 221003
24.6%
ValueCountFrequency (%)
0 6631
 
0.7%
1 154254
17.2%
2 138297
15.4%
3 90674
10.1%
4 73644
8.2%
5 60319
 
6.7%
6 45759
 
5.1%
7 31495
 
3.5%
8 31361
 
3.5%
9 18131
 
2.0%
ValueCountFrequency (%)
9999 4
< 0.1%
9992 1
 
< 0.1%
9945 1
 
< 0.1%
9090 1
 
< 0.1%
9000 2
 
< 0.1%
8500 1
 
< 0.1%
8041 1
 
< 0.1%
8018 1
 
< 0.1%
8000 7
< 0.1%
7999 1
 
< 0.1%

NewExist
Categorical

Distinct3
Distinct (%)< 0.1%
Missing136
Missing (%)< 0.1%
Memory size6.9 MiB
1.0
644869 
2.0
253125 
0.0
 
1034

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2697084
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 644869
71.7%
2.0 253125
 
28.2%
0.0 1034
 
0.1%
(Missing) 136
 
< 0.1%

Length

2025-02-06T16:23:24.451676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-06T16:23:24.514131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 644869
71.7%
2.0 253125
 
28.2%
0.0 1034
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 900062
33.4%
. 899028
33.3%
1 644869
23.9%
2 253125
 
9.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2697084
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 900062
33.4%
. 899028
33.3%
1 644869
23.9%
2 253125
 
9.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2697084
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 900062
33.4%
. 899028
33.3%
1 644869
23.9%
2 253125
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2697084
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 900062
33.4%
. 899028
33.3%
1 644869
23.9%
2 253125
 
9.4%

CreateJob
Real number (ℝ)

Skewed  Zeros 

Distinct246
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4303764
Minimum0
Maximum8800
Zeros629248
Zeros (%)70.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2025-02-06T16:23:24.586235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile10
Maximum8800
Range8800
Interquartile range (IQR)1

Descriptive statistics

Standard deviation236.68817
Coefficient of variation (CV)28.075634
Kurtosis1369.911
Mean8.4303764
Median Absolute Deviation (MAD)0
Skewness36.991355
Sum7580291
Variance56021.288
MonotonicityNot monotonic
2025-02-06T16:23:24.675249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 629248
70.0%
1 63174
 
7.0%
2 57831
 
6.4%
3 28806
 
3.2%
4 20511
 
2.3%
5 18691
 
2.1%
10 11602
 
1.3%
6 11009
 
1.2%
8 7378
 
0.8%
7 6374
 
0.7%
Other values (236) 44540
 
5.0%
ValueCountFrequency (%)
0 629248
70.0%
1 63174
 
7.0%
2 57831
 
6.4%
3 28806
 
3.2%
4 20511
 
2.3%
5 18691
 
2.1%
6 11009
 
1.2%
7 6374
 
0.7%
8 7378
 
0.8%
9 3330
 
0.4%
ValueCountFrequency (%)
8800 648
0.1%
5621 1
 
< 0.1%
5199 1
 
< 0.1%
5085 1
 
< 0.1%
3500 1
 
< 0.1%
3100 1
 
< 0.1%
3000 4
 
< 0.1%
2515 1
 
< 0.1%
2140 1
 
< 0.1%
2020 1
 
< 0.1%

RetainedJob
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct358
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.797257
Minimum0
Maximum9500
Zeros440403
Zeros (%)49.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2025-02-06T16:23:24.761752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile20
Maximum9500
Range9500
Interquartile range (IQR)4

Descriptive statistics

Standard deviation237.1206
Coefficient of variation (CV)21.961188
Kurtosis1362.0182
Mean10.797257
Median Absolute Deviation (MAD)1
Skewness36.854812
Sum9708505
Variance56226.179
MonotonicityNot monotonic
2025-02-06T16:23:24.850347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 440403
49.0%
1 88790
 
9.9%
2 76851
 
8.5%
3 49963
 
5.6%
4 39666
 
4.4%
5 32627
 
3.6%
6 23796
 
2.6%
7 16530
 
1.8%
8 15698
 
1.7%
10 15438
 
1.7%
Other values (348) 99402
 
11.1%
ValueCountFrequency (%)
0 440403
49.0%
1 88790
 
9.9%
2 76851
 
8.5%
3 49963
 
5.6%
4 39666
 
4.4%
5 32627
 
3.6%
6 23796
 
2.6%
7 16530
 
1.8%
8 15698
 
1.7%
9 8735
 
1.0%
ValueCountFrequency (%)
9500 1
 
< 0.1%
8800 648
0.1%
7250 1
 
< 0.1%
5000 1
 
< 0.1%
4441 1
 
< 0.1%
4000 2
 
< 0.1%
3900 1
 
< 0.1%
3860 1
 
< 0.1%
3225 1
 
< 0.1%
3200 1
 
< 0.1%

FranchiseCode
Real number (ℝ)

Zeros 

Distinct2768
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2753.7259
Minimum0
Maximum99999
Zeros208835
Zeros (%)23.2%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2025-02-06T16:23:24.938652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile15805
Maximum99999
Range99999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation12758.019
Coefficient of variation (CV)4.6330025
Kurtosis24.409524
Mean2753.7259
Median Absolute Deviation (MAD)0
Skewness4.9752152
Sum2.4760512 × 109
Variance1.6276705 × 108
MonotonicityNot monotonic
2025-02-06T16:23:25.027940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 638554
71.0%
0 208835
 
23.2%
78760 3373
 
0.4%
68020 1921
 
0.2%
50564 1034
 
0.1%
21780 1003
 
0.1%
25650 715
 
0.1%
79140 659
 
0.1%
22470 615
 
0.1%
17998 606
 
0.1%
Other values (2758) 41849
 
4.7%
ValueCountFrequency (%)
0 208835
 
23.2%
1 638554
71.0%
3 12
 
< 0.1%
395 5
 
< 0.1%
399 3
 
< 0.1%
400 2
 
< 0.1%
401 12
 
< 0.1%
404 1
 
< 0.1%
407 34
 
< 0.1%
414 2
 
< 0.1%
ValueCountFrequency (%)
99999 1
 
< 0.1%
92006 4
 
< 0.1%
92000 9
< 0.1%
91999 11
< 0.1%
91450 2
 
< 0.1%
91446 1
 
< 0.1%
91443 2
 
< 0.1%
91435 1
 
< 0.1%
91424 1
 
< 0.1%
91423 2
 
< 0.1%

UrbanRural
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
1
470654 
0
323167 
2
105343 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters899164
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 470654
52.3%
0 323167
35.9%
2 105343
 
11.7%

Length

2025-02-06T16:23:25.107836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-06T16:23:25.156728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 470654
52.3%
0 323167
35.9%
2 105343
 
11.7%

Most occurring characters

ValueCountFrequency (%)
1 470654
52.3%
0 323167
35.9%
2 105343
 
11.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 899164
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 470654
52.3%
0 323167
35.9%
2 105343
 
11.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 899164
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 470654
52.3%
0 323167
35.9%
2 105343
 
11.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 899164
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 470654
52.3%
0 323167
35.9%
2 105343
 
11.7%

RevLineCr
Categorical

Imbalance 

Distinct18
Distinct (%)< 0.1%
Missing4528
Missing (%)0.5%
Memory size6.9 MiB
N
420288 
0
257602 
Y
201397 
T
 
15284
1
 
23
Other values (13)
 
42

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters894636
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 420288
46.7%
0 257602
28.6%
Y 201397
22.4%
T 15284
 
1.7%
1 23
 
< 0.1%
R 14
 
< 0.1%
` 11
 
< 0.1%
2 6
 
< 0.1%
C 2
 
< 0.1%
, 1
 
< 0.1%
Other values (8) 8
 
< 0.1%
(Missing) 4528
 
0.5%

Length

2025-02-06T16:23:25.219020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
n 420288
47.0%
0 257602
28.8%
y 201397
22.5%
t 15284
 
1.7%
1 23
 
< 0.1%
r 14
 
< 0.1%
14
 
< 0.1%
2 6
 
< 0.1%
c 2
 
< 0.1%
3 1
 
< 0.1%
Other values (5) 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 420288
47.0%
0 257602
28.8%
Y 201397
22.5%
T 15284
 
1.7%
1 23
 
< 0.1%
R 14
 
< 0.1%
` 11
 
< 0.1%
2 6
 
< 0.1%
C 2
 
< 0.1%
, 1
 
< 0.1%
Other values (8) 8
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 894636
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 420288
47.0%
0 257602
28.8%
Y 201397
22.5%
T 15284
 
1.7%
1 23
 
< 0.1%
R 14
 
< 0.1%
` 11
 
< 0.1%
2 6
 
< 0.1%
C 2
 
< 0.1%
, 1
 
< 0.1%
Other values (8) 8
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 894636
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 420288
47.0%
0 257602
28.8%
Y 201397
22.5%
T 15284
 
1.7%
1 23
 
< 0.1%
R 14
 
< 0.1%
` 11
 
< 0.1%
2 6
 
< 0.1%
C 2
 
< 0.1%
, 1
 
< 0.1%
Other values (8) 8
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 894636
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 420288
47.0%
0 257602
28.8%
Y 201397
22.5%
T 15284
 
1.7%
1 23
 
< 0.1%
R 14
 
< 0.1%
` 11
 
< 0.1%
2 6
 
< 0.1%
C 2
 
< 0.1%
, 1
 
< 0.1%
Other values (8) 8
 
< 0.1%

LowDoc
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size878.2 KiB
False
785060 
True
114104 
ValueCountFrequency (%)
False 785060
87.3%
True 114104
 
12.7%
2025-02-06T16:23:25.265695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

ChgOffDate
Date

Missing 

Distinct6448
Distinct (%)4.0%
Missing736465
Missing (%)81.9%
Memory size6.9 MiB
Minimum1988-10-03 00:00:00
Maximum2026-10-22 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-06T16:23:25.334197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:25.437477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct8472
Distinct (%)0.9%
Missing2368
Missing (%)0.3%
Memory size6.9 MiB
Minimum1975-01-17 00:00:00
Maximum2074-12-04 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-06T16:23:25.533324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:25.628690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct118859
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
2025-02-06T16:23:25.928162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length15
Median length14
Mean length11.537586
Min length6

Characters and Unicode

Total characters10374182
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique79785 ?
Unique (%)8.9%

Sample

1st row$60,000.00
2nd row$40,000.00
3rd row$287,000.00
4th row$35,000.00
5th row$229,000.00
ValueCountFrequency (%)
50,000.00 43787
 
4.9%
100,000.00 36714
 
4.1%
25,000.00 27387
 
3.0%
150,000.00 23373
 
2.6%
10,000.00 21328
 
2.4%
35,000.00 14748
 
1.6%
5,000.00 14193
 
1.6%
75,000.00 13528
 
1.5%
20,000.00 13462
 
1.5%
30,000.00 12696
 
1.4%
Other values (118849) 677948
75.4%
2025-02-06T16:23:26.295997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4457089
43.0%
, 924978
 
8.9%
. 899164
 
8.7%
$ 899164
 
8.7%
899164
 
8.7%
5 445569
 
4.3%
1 409947
 
4.0%
2 312909
 
3.0%
3 238773
 
2.3%
4 207077
 
2.0%
Other values (4) 680348
 
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10374182
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4457089
43.0%
, 924978
 
8.9%
. 899164
 
8.7%
$ 899164
 
8.7%
899164
 
8.7%
5 445569
 
4.3%
1 409947
 
4.0%
2 312909
 
3.0%
3 238773
 
2.3%
4 207077
 
2.0%
Other values (4) 680348
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10374182
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4457089
43.0%
, 924978
 
8.9%
. 899164
 
8.7%
$ 899164
 
8.7%
899164
 
8.7%
5 445569
 
4.3%
1 409947
 
4.0%
2 312909
 
3.0%
3 238773
 
2.3%
4 207077
 
2.0%
Other values (4) 680348
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10374182
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4457089
43.0%
, 924978
 
8.9%
. 899164
 
8.7%
$ 899164
 
8.7%
899164
 
8.7%
5 445569
 
4.3%
1 409947
 
4.0%
2 312909
 
3.0%
3 238773
 
2.3%
4 207077
 
2.0%
Other values (4) 680348
 
6.6%

BalanceGross
Categorical

Imbalance 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
$0.00
899150 
$12,750.00
 
1
$827,875.00
 
1
$25,000.00
 
1
$37,100.00
 
1
Other values (10)
 
10

Length

Max length12
Median length6
Mean length6.0000767
Min length6

Characters and Unicode

Total characters5395053
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)< 0.1%

Sample

1st row$0.00
2nd row$0.00
3rd row$0.00
4th row$0.00
5th row$0.00

Common Values

ValueCountFrequency (%)
$0.00 899150
> 99.9%
$12,750.00 1
 
< 0.1%
$827,875.00 1
 
< 0.1%
$25,000.00 1
 
< 0.1%
$37,100.00 1
 
< 0.1%
$43,127.00 1
 
< 0.1%
$84,617.00 1
 
< 0.1%
$1,760.00 1
 
< 0.1%
$115,820.00 1
 
< 0.1%
$996,262.00 1
 
< 0.1%
Other values (5) 5
 
< 0.1%

Length

2025-02-06T16:23:26.380512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0.00 899150
> 99.9%
12,750.00 1
 
< 0.1%
827,875.00 1
 
< 0.1%
25,000.00 1
 
< 0.1%
37,100.00 1
 
< 0.1%
43,127.00 1
 
< 0.1%
84,617.00 1
 
< 0.1%
1,760.00 1
 
< 0.1%
115,820.00 1
 
< 0.1%
996,262.00 1
 
< 0.1%
Other values (5) 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 2697490
50.0%
$ 899164
 
16.7%
. 899164
 
16.7%
899164
 
16.7%
, 13
 
< 0.1%
1 11
 
< 0.1%
7 8
 
< 0.1%
2 7
 
< 0.1%
6 7
 
< 0.1%
9 7
 
< 0.1%
Other values (4) 18
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5395053
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2697490
50.0%
$ 899164
 
16.7%
. 899164
 
16.7%
899164
 
16.7%
, 13
 
< 0.1%
1 11
 
< 0.1%
7 8
 
< 0.1%
2 7
 
< 0.1%
6 7
 
< 0.1%
9 7
 
< 0.1%
Other values (4) 18
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5395053
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2697490
50.0%
$ 899164
 
16.7%
. 899164
 
16.7%
899164
 
16.7%
, 13
 
< 0.1%
1 11
 
< 0.1%
7 8
 
< 0.1%
2 7
 
< 0.1%
6 7
 
< 0.1%
9 7
 
< 0.1%
Other values (4) 18
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5395053
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2697490
50.0%
$ 899164
 
16.7%
. 899164
 
16.7%
899164
 
16.7%
, 13
 
< 0.1%
1 11
 
< 0.1%
7 8
 
< 0.1%
2 7
 
< 0.1%
6 7
 
< 0.1%
9 7
 
< 0.1%
Other values (4) 18
 
< 0.1%

MIS_Status
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
P I F
741345 
CHGOFF
157819 

Length

Max length6
Median length5
Mean length5.1755175
Min length5

Characters and Unicode

Total characters4653639
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowP I F
2nd rowP I F
3rd rowP I F
4th rowP I F
5th rowP I F

Common Values

ValueCountFrequency (%)
P I F 741345
82.4%
CHGOFF 157819
 
17.6%

Length

2025-02-06T16:23:26.456820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-06T16:23:26.507312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
p 741345
31.1%
i 741345
31.1%
f 741345
31.1%
chgoff 157819
 
6.6%

Most occurring characters

ValueCountFrequency (%)
1482690
31.9%
F 1056983
22.7%
P 741345
15.9%
I 741345
15.9%
C 157819
 
3.4%
H 157819
 
3.4%
G 157819
 
3.4%
O 157819
 
3.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4653639
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1482690
31.9%
F 1056983
22.7%
P 741345
15.9%
I 741345
15.9%
C 157819
 
3.4%
H 157819
 
3.4%
G 157819
 
3.4%
O 157819
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4653639
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1482690
31.9%
F 1056983
22.7%
P 741345
15.9%
I 741345
15.9%
C 157819
 
3.4%
H 157819
 
3.4%
G 157819
 
3.4%
O 157819
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4653639
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1482690
31.9%
F 1056983
22.7%
P 741345
15.9%
I 741345
15.9%
C 157819
 
3.4%
H 157819
 
3.4%
G 157819
 
3.4%
O 157819
 
3.4%
Distinct83165
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
2025-02-06T16:23:26.704077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length14
Median length6
Mean length6.8997235
Min length6

Characters and Unicode

Total characters6203983
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52342 ?
Unique (%)5.8%

Sample

1st row$0.00
2nd row$0.00
3rd row$0.00
4th row$0.00
5th row$0.00
ValueCountFrequency (%)
0.00 737152
82.0%
50,000.00 2110
 
0.2%
10,000.00 1865
 
0.2%
25,000.00 1371
 
0.2%
35,000.00 1345
 
0.1%
100,000.00 1028
 
0.1%
20,000.00 594
 
0.1%
30,000.00 492
 
0.1%
15,000.00 467
 
0.1%
5,000.00 356
 
< 0.1%
Other values (83155) 152384
 
16.9%
2025-02-06T16:23:27.011853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2643222
42.6%
$ 899164
 
14.5%
. 899164
 
14.5%
899164
 
14.5%
, 161591
 
2.6%
1 98607
 
1.6%
2 88727
 
1.4%
4 86077
 
1.4%
9 81470
 
1.3%
3 79226
 
1.3%
Other values (4) 267571
 
4.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6203983
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2643222
42.6%
$ 899164
 
14.5%
. 899164
 
14.5%
899164
 
14.5%
, 161591
 
2.6%
1 98607
 
1.6%
2 88727
 
1.4%
4 86077
 
1.4%
9 81470
 
1.3%
3 79226
 
1.3%
Other values (4) 267571
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6203983
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2643222
42.6%
$ 899164
 
14.5%
. 899164
 
14.5%
899164
 
14.5%
, 161591
 
2.6%
1 98607
 
1.6%
2 88727
 
1.4%
4 86077
 
1.4%
9 81470
 
1.3%
3 79226
 
1.3%
Other values (4) 267571
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6203983
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2643222
42.6%
$ 899164
 
14.5%
. 899164
 
14.5%
899164
 
14.5%
, 161591
 
2.6%
1 98607
 
1.6%
2 88727
 
1.4%
4 86077
 
1.4%
9 81470
 
1.3%
3 79226
 
1.3%
Other values (4) 267571
 
4.3%

GrAppv
Text

Distinct22128
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
2025-02-06T16:23:27.183780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length14
Median length12
Mean length11.513319
Min length8

Characters and Unicode

Total characters10352362
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13651 ?
Unique (%)1.5%

Sample

1st row$60,000.00
2nd row$40,000.00
3rd row$287,000.00
4th row$35,000.00
5th row$229,000.00
ValueCountFrequency (%)
50,000.00 69394
 
7.7%
25,000.00 51258
 
5.7%
100,000.00 50977
 
5.7%
10,000.00 38366
 
4.3%
150,000.00 27624
 
3.1%
20,000.00 23434
 
2.6%
35,000.00 23181
 
2.6%
30,000.00 21004
 
2.3%
5,000.00 19146
 
2.1%
15,000.00 18472
 
2.1%
Other values (22118) 556308
61.9%
2025-02-06T16:23:27.420178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4946152
47.8%
, 925342
 
8.9%
. 899164
 
8.7%
$ 899164
 
8.7%
899164
 
8.7%
5 450225
 
4.3%
1 345271
 
3.3%
2 266534
 
2.6%
3 180629
 
1.7%
4 133995
 
1.3%
Other values (4) 406722
 
3.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10352362
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4946152
47.8%
, 925342
 
8.9%
. 899164
 
8.7%
$ 899164
 
8.7%
899164
 
8.7%
5 450225
 
4.3%
1 345271
 
3.3%
2 266534
 
2.6%
3 180629
 
1.7%
4 133995
 
1.3%
Other values (4) 406722
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10352362
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4946152
47.8%
, 925342
 
8.9%
. 899164
 
8.7%
$ 899164
 
8.7%
899164
 
8.7%
5 450225
 
4.3%
1 345271
 
3.3%
2 266534
 
2.6%
3 180629
 
1.7%
4 133995
 
1.3%
Other values (4) 406722
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10352362
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4946152
47.8%
, 925342
 
8.9%
. 899164
 
8.7%
$ 899164
 
8.7%
899164
 
8.7%
5 450225
 
4.3%
1 345271
 
3.3%
2 266534
 
2.6%
3 180629
 
1.7%
4 133995
 
1.3%
Other values (4) 406722
 
3.9%
Distinct38326
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
2025-02-06T16:23:27.634505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length14
Median length11
Mean length11.308074
Min length8

Characters and Unicode

Total characters10167813
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23260 ?
Unique (%)2.6%

Sample

1st row$48,000.00
2nd row$32,000.00
3rd row$215,250.00
4th row$28,000.00
5th row$229,000.00
ValueCountFrequency (%)
25,000.00 49579
 
5.5%
12,500.00 40147
 
4.5%
5,000.00 31135
 
3.5%
50,000.00 25047
 
2.8%
10,000.00 17009
 
1.9%
17,500.00 16141
 
1.8%
15,000.00 14490
 
1.6%
7,500.00 12781
 
1.4%
127,500.00 11946
 
1.3%
80,000.00 10965
 
1.2%
Other values (38316) 669924
74.5%
2025-02-06T16:23:27.929954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4048030
39.8%
, 908994
 
8.9%
. 899164
 
8.8%
$ 899164
 
8.8%
899164
 
8.8%
5 654346
 
6.4%
2 433556
 
4.3%
1 386969
 
3.8%
7 251493
 
2.5%
3 186643
 
1.8%
Other values (4) 600290
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10167813
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4048030
39.8%
, 908994
 
8.9%
. 899164
 
8.8%
$ 899164
 
8.8%
899164
 
8.8%
5 654346
 
6.4%
2 433556
 
4.3%
1 386969
 
3.8%
7 251493
 
2.5%
3 186643
 
1.8%
Other values (4) 600290
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10167813
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4048030
39.8%
, 908994
 
8.9%
. 899164
 
8.8%
$ 899164
 
8.8%
899164
 
8.8%
5 654346
 
6.4%
2 433556
 
4.3%
1 386969
 
3.8%
7 251493
 
2.5%
3 186643
 
1.8%
Other values (4) 600290
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10167813
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4048030
39.8%
, 908994
 
8.9%
. 899164
 
8.8%
$ 899164
 
8.8%
899164
 
8.8%
5 654346
 
6.4%
2 433556
 
4.3%
1 386969
 
3.8%
7 251493
 
2.5%
3 186643
 
1.8%
Other values (4) 600290
 
5.9%

cat_activites
Real number (ℝ)

High correlation  Zeros 

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.612263
Minimum0
Maximum92
Zeros201948
Zeros (%)22.5%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2025-02-06T16:23:28.007949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q123
median44
Q356
95-th percentile81
Maximum92
Range92
Interquartile range (IQR)33

Descriptive statistics

Standard deviation26.284706
Coefficient of variation (CV)0.66354972
Kurtosis-1.0572678
Mean39.612263
Median Absolute Deviation (MAD)18
Skewness-0.24819754
Sum35617921
Variance690.88577
MonotonicityNot monotonic
2025-02-06T16:23:28.074213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 201948
22.5%
44 84737
9.4%
81 72618
 
8.1%
54 68170
 
7.6%
72 67600
 
7.5%
23 66646
 
7.4%
62 55366
 
6.2%
42 48743
 
5.4%
45 42514
 
4.7%
33 38284
 
4.3%
Other values (15) 152538
17.0%
ValueCountFrequency (%)
0 201948
22.5%
11 9005
 
1.0%
21 1851
 
0.2%
22 663
 
0.1%
23 66646
 
7.4%
31 11809
 
1.3%
32 17936
 
2.0%
33 38284
 
4.3%
42 48743
 
5.4%
44 84737
9.4%
ValueCountFrequency (%)
92 229
 
< 0.1%
81 72618
8.1%
72 67600
7.5%
71 14640
 
1.6%
62 55366
6.2%
61 6425
 
0.7%
56 32685
3.6%
55 257
 
< 0.1%
54 68170
7.6%
53 13632
 
1.5%

SBA_loan_float
Real number (ℝ)

High correlation 

Distinct38326
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean149488.79
Minimum100
Maximum5472000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2025-02-06T16:23:28.356377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile5000
Q121250
median61250
Q3175000
95-th percentile626250
Maximum5472000
Range5471900
Interquartile range (IQR)153750

Descriptive statistics

Standard deviation228414.56
Coefficient of variation (CV)1.5279712
Kurtosis25.325514
Mean149488.79
Median Absolute Deviation (MAD)48750
Skewness3.6752753
Sum1.3441494 × 1011
Variance5.2173212 × 1010
MonotonicityNot monotonic
2025-02-06T16:23:28.441600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25000 49579
 
5.5%
12500 40147
 
4.5%
5000 31135
 
3.5%
50000 25047
 
2.8%
10000 17009
 
1.9%
17500 16141
 
1.8%
15000 14490
 
1.6%
7500 12781
 
1.4%
127500 11946
 
1.3%
80000 10965
 
1.2%
Other values (38316) 669924
74.5%
ValueCountFrequency (%)
100 2
 
< 0.1%
150 1
 
< 0.1%
200 2
 
< 0.1%
250 33
 
< 0.1%
350 4
 
< 0.1%
400 4
 
< 0.1%
475 1
 
< 0.1%
500 442
< 0.1%
600 12
 
< 0.1%
650 2
 
< 0.1%
ValueCountFrequency (%)
5472000 1
 
< 0.1%
5000000 1
 
< 0.1%
4869000 1
 
< 0.1%
4582000 1
 
< 0.1%
4500000 23
< 0.1%
4492530 1
 
< 0.1%
4410000 1
 
< 0.1%
4320000 1
 
< 0.1%
4050000 4
 
< 0.1%
4000000 13
< 0.1%

bank_loan_float
Real number (ℝ)

High correlation 

Distinct22128
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean192686.98
Minimum200
Maximum5472000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2025-02-06T16:23:28.526648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum200
5-th percentile10000
Q135000
median90000
Q3225000
95-th percentile750000
Maximum5472000
Range5471800
Interquartile range (IQR)190000

Descriptive statistics

Standard deviation283263.39
Coefficient of variation (CV)1.4700702
Kurtosis21.018882
Mean192686.98
Median Absolute Deviation (MAD)65000
Skewness3.5207901
Sum1.7325719 × 1011
Variance8.0238149 × 1010
MonotonicityNot monotonic
2025-02-06T16:23:28.612445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50000 69394
 
7.7%
25000 51258
 
5.7%
100000 50977
 
5.7%
10000 38366
 
4.3%
150000 27624
 
3.1%
20000 23434
 
2.6%
35000 23181
 
2.6%
30000 21004
 
2.3%
5000 19146
 
2.1%
15000 18472
 
2.1%
Other values (22118) 556308
61.9%
ValueCountFrequency (%)
200 2
 
< 0.1%
300 1
 
< 0.1%
400 2
 
< 0.1%
500 33
 
< 0.1%
700 4
 
< 0.1%
800 4
 
< 0.1%
950 1
 
< 0.1%
1000 444
< 0.1%
1200 12
 
< 0.1%
1300 2
 
< 0.1%
ValueCountFrequency (%)
5472000 1
 
< 0.1%
5000000 40
< 0.1%
4991700 1
 
< 0.1%
4950000 1
 
< 0.1%
4908500 1
 
< 0.1%
4900000 2
 
< 0.1%
4872000 1
 
< 0.1%
4869000 1
 
< 0.1%
4830000 1
 
< 0.1%
4800000 1
 
< 0.1%

crisis
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
2
711162 
1
188002 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters899164
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 711162
79.1%
1 188002
 
20.9%

Length

2025-02-06T16:23:28.689351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-06T16:23:28.733949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 711162
79.1%
1 188002
 
20.9%

Most occurring characters

ValueCountFrequency (%)
2 711162
79.1%
1 188002
 
20.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 899164
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 711162
79.1%
1 188002
 
20.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 899164
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 711162
79.1%
1 188002
 
20.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 899164
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 711162
79.1%
1 188002
 
20.9%

Interactions

2025-02-06T16:23:09.094831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:46.477346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:48.483759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:50.696303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:52.713370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:54.779316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:56.807565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:58.972172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:00.904164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:02.861346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:04.866708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:07.095426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:09.264303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:46.671677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:48.647902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:50.869729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:52.886223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:54.950558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:56.977729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:59.136301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:01.071427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:03.030852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:05.033944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:07.260917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:09.431209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:46.838197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:48.971826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:51.037281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:53.063388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:55.119545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:57.144998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:59.297275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:01.232092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:03.197434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:05.203115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:07.428670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:09.602350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:47.004348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:49.142383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:51.207107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:53.232198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:55.286816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:57.499326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:59.455918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:01.394506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:03.365900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:05.586987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:07.592374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:09.770019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:47.165759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:49.315147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:51.374157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:53.407734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:55.456118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:57.660737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:59.618469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:01.559891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:03.531436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:05.754971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:07.761847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:09.941648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:47.329739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:49.481925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:51.540798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:53.582229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:55.621770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:57.822216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:59.779684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:01.718789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:03.697714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:05.922363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:07.928340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:10.111778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:47.494986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:49.654605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:51.705026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:53.753834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:55.793402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:57.985711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:59.936884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:01.882644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:03.862095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:06.089534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:08.097452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:10.277137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:47.657054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:49.825953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:51.870732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:53.922446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:55.962020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:58.149653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:00.097765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:02.038988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:04.026079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:06.253394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:08.258616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:10.442707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:47.820342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:50.000153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:52.037853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:54.093610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:56.133725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:58.311258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:00.260607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:02.205482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:04.192065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:06.418217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:08.427228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:10.638310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:47.987879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:50.174567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:52.212255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:54.269601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:56.306263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:58.477699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:00.420555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:02.374486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:04.363145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:06.587731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:08.594027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:10.805190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:48.147695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:50.347412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:52.376105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:54.438313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:56.474602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:58.637800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:00.579234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:02.531749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:04.524957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:06.752657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:08.757973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:10.966066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:48.314021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:50.520470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:52.543014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:54.606266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:56.643012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:22:58.805052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:00.742714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:02.695511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:04.691602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:06.927147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T16:23:08.925973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-06T16:23:28.799972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ApprovalFYBalanceGrossCreateJobFranchiseCodeLoanNr_ChkDgtLowDocMIS_StatusNAICSNewExistNoEmpRetainedJobRevLineCrSBA_loan_floatTermUrbanRuralZipbank_loan_floatcat_activitescrisis
ApprovalFY1.0000.0000.268-0.452-0.2780.3630.3270.4470.065-0.2260.5460.209-0.366-0.2970.659-0.038-0.3000.4400.412
BalanceGross0.0001.0000.0000.0050.0010.0000.0000.0010.0000.0000.0000.0000.0000.0000.0020.0010.0000.0010.000
CreateJob0.2680.0001.000-0.054-0.0310.0100.0120.1570.0090.0340.3770.0110.0780.0820.0250.0260.0930.1560.014
FranchiseCode-0.4520.005-0.0541.0000.3920.0350.022-0.0910.0990.121-0.2630.0440.2850.1960.0130.0310.259-0.0850.017
LoanNr_ChkDgt-0.2780.001-0.0310.3921.0000.2400.237-0.0500.0620.075-0.1420.0840.1690.1210.1890.0310.139-0.0470.573
LowDoc0.3630.0000.0100.0350.2401.0000.0770.1470.1640.0030.0100.2190.0990.1710.2060.1450.1180.1470.069
MIS_Status0.3270.0000.0120.0220.2370.0771.0000.1480.0220.0040.0130.1450.0700.4910.2100.0800.0740.1480.172
NAICS0.4470.0010.157-0.091-0.0500.1470.1481.0000.094-0.1540.2710.124-0.175-0.0810.432-0.034-0.1470.9980.163
NewExist0.0650.0000.0090.0990.0620.1640.0220.0941.0000.0050.0020.0650.0290.0880.0300.0880.0360.0940.038
NoEmp-0.2260.0000.0340.1210.0750.0030.004-0.1540.0051.0000.1240.0000.4490.2000.0100.0590.455-0.1510.004
RetainedJob0.5460.0000.377-0.263-0.1420.0100.0130.2710.0020.1241.0000.010-0.205-0.1570.025-0.026-0.1380.2680.014
RevLineCr0.2090.0000.0110.0440.0840.2190.1450.1240.0650.0000.0101.0000.0500.1400.3480.0560.0570.1240.161
SBA_loan_float-0.3660.0000.0780.2850.1690.0990.070-0.1750.0290.449-0.2050.0501.0000.5890.0510.1310.986-0.1690.016
Term-0.2970.0000.0820.1960.1210.1710.491-0.0810.0880.200-0.1570.1400.5891.0000.2070.1420.558-0.0760.105
UrbanRural0.6590.0020.0250.0130.1890.2060.2100.4320.0300.0100.0250.3480.0510.2071.0000.1260.0510.4320.367
Zip-0.0380.0010.0260.0310.0310.1450.080-0.0340.0880.059-0.0260.0560.1310.1420.1261.0000.119-0.0330.033
bank_loan_float-0.3000.0000.0930.2590.1390.1180.074-0.1470.0360.455-0.1380.0570.9860.5580.0510.1191.000-0.1420.017
cat_activites0.4400.0010.156-0.085-0.0470.1470.1480.9980.094-0.1510.2680.124-0.169-0.0760.432-0.033-0.1421.0000.163
crisis0.4120.0000.0140.0170.5730.0690.1720.1630.0380.0040.0140.1610.0160.1050.3670.0330.0170.1631.000

Missing values

2025-02-06T16:23:11.505673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-06T16:23:14.018080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-02-06T16:23:18.418823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

LoanNr_ChkDgtNameCityStateZipBankBankStateNAICSApprovalDateApprovalFYTermNoEmpNewExistCreateJobRetainedJobFranchiseCodeUrbanRuralRevLineCrLowDocChgOffDateDisbursementDateDisbursementGrossBalanceGrossMIS_StatusChgOffPrinGrGrAppvSBA_Appvcat_activitesSBA_loan_floatbank_loan_floatcrisis
01000014003ABC HOBBYCRAFTEVANSVILLEIN47711FIFTH THIRD BANKOH4511201997-02-2819978442.00010NYNaN28-Feb-99$60,000.00$0.00P I F$0.00$60,000.00$48,000.004548000.060000.02
11000024006LANDMARK BAR & GRILLE (THE)NEW PARISIN465261ST SOURCE BANKIN7224101997-02-2819976022.00010NYNaN31-May-97$40,000.00$0.00P I F$0.00$40,000.00$32,000.007232000.040000.02
21000034009WHITLOCK DDS, TODD M.BLOOMINGTONIN47401GRANT COUNTY STATE BANKIN6212101997-02-28199718071.00010NNNaN31-Dec-97$287,000.00$0.00P I F$0.00$287,000.00$215,250.0062215250.0287000.02
31000044001BIG BUCKS PAWN & JEWELRY, LLCBROKEN ARROWOK740121ST NATL BK & TR CO OF BROKENOK01997-02-2819976021.00010NYNaN30-Jun-97$35,000.00$0.00P I F$0.00$35,000.00$28,000.00028000.035000.02
41000054004ANASTASIA CONFECTIONS, INC.ORLANDOFL32801FLORIDA BUS. DEVEL CORPFL01997-02-281997240141.07710NNNaN14-May-97$229,000.00$0.00P I F$0.00$229,000.00$229,000.000229000.0229000.02
51000084002B&T SCREW MACHINE COMPANY, INCPLAINVILLECT6062TD BANK, NATIONAL ASSOCIATIONDE3327211997-02-281997120191.00010NNNaN30-Jun-97$517,000.00$0.00P I F$0.00$517,000.00$387,750.0033387750.0517000.02
61000093009MIDDLE ATLANTIC SPORTS CO INCUNIONNJ7083WELLS FARGO BANK NATL ASSOCSD01980-06-02198045452.00000NN24-Jun-9122-Jul-80$600,000.00$0.00CHGOFF$208,959.00$600,000.00$499,998.000499998.0600000.01
71000094005WEAVER PRODUCTSSUMMERFIELDFL34491REGIONS BANKAL8111181997-02-2819978412.00010NYNaN30-Jun-98$45,000.00$0.00P I F$0.00$45,000.00$36,000.008136000.045000.02
81000104006TURTLE BEACH INNPORT SAINT JOEFL32456CENTENNIAL BANKFL7213101997-02-28199729722.00010NNNaN31-Jul-97$305,000.00$0.00P I F$0.00$305,000.00$228,750.0072228750.0305000.02
91000124001INTEXT BUILDING SYS LLCGLASTONBURYCT6073WEBSTER BANK NATL ASSOCCT01997-02-2819978432.00010NYNaN30-Apr-97$70,000.00$0.00P I F$0.00$70,000.00$56,000.00056000.070000.02
LoanNr_ChkDgtNameCityStateZipBankBankStateNAICSApprovalDateApprovalFYTermNoEmpNewExistCreateJobRetainedJobFranchiseCodeUrbanRuralRevLineCrLowDocChgOffDateDisbursementDateDisbursementGrossBalanceGrossMIS_StatusChgOffPrinGrGrAppvSBA_Appvcat_activitesSBA_loan_floatbank_loan_floatcrisis
8991549995423005LITWIN LIVERY SERVICES, INC.CAMPBELLOH44405JPMORGAN CHASE BANK NATL ASSOCIL01997-02-2719976011.000100NNaN30-Sep-97$10,000.00$0.00P I F$0.00$10,000.00$5,000.0005000.010000.02
8991559995453003FUTURE LEADERS CENTER, INC.SO. OZONE PARKNY11420FLUSHING BANKNY6244101997-02-27199718021.000100NNaN30-Jun-97$123,000.00$0.00P I F$0.00$128,000.00$96,000.006296000.0128000.02
8991569995473009FABRICATORS STEEL, INC.BALTIMOREMD21224BANK OF AMERICA NATL ASSOCMD3324311997-02-27199760201.000100NNaN30-Jun-97$50,000.00$0.00P I F$0.00$50,000.00$25,000.003325000.050000.02
8991579995493004PULLTARPS MFG.EL CAJONCA92020U.S. BANK NATIONAL ASSOCIATIONCA3149121997-02-27199736401.00010NNNaN31-Mar-97$200,000.00$0.00P I F$0.00$200,000.00$150,000.0031150000.0200000.02
8991589995563001SHADES WINDOW TINTING AUTO ALAIRVINGTX75062LOANS FROM OLD CLOSED LENDERSDC01997-02-2719978452.00010NYNaN30-Jun-97$79,000.00$0.00P I F$0.00$79,000.00$63,200.00063200.079000.02
8991599995573004FABRIC FARMSUPPER ARLINGTONOH43221JPMORGAN CHASE BANK NATL ASSOCIL4511201997-02-2719976061.000100NNaN30-Sep-97$70,000.00$0.00P I F$0.00$70,000.00$56,000.004556000.070000.02
8991609995603000FABRIC FARMSCOLUMBUSOH43221JPMORGAN CHASE BANK NATL ASSOCIL4511301997-02-2719976061.00010YNNaN31-Oct-97$85,000.00$0.00P I F$0.00$85,000.00$42,500.004542500.085000.02
8991619995613003RADCO MANUFACTURING CO.,INC.SANTA MARIACA93455RABOBANK, NATIONAL ASSOCIATIONCA3323211997-02-271997108261.00010NNNaN30-Sep-97$300,000.00$0.00P I F$0.00$300,000.00$225,000.0033225000.0300000.02
8991629995973006MARUTAMA HAWAII, INC.HONOLULUHI96830BANK OF HAWAIIHI01997-02-2719976061.00010NY8-Mar-0031-Mar-97$75,000.00$0.00CHGOFF$46,383.00$75,000.00$60,000.00060000.075000.02
8991639996003010PACIFIC TRADEWINDS FAN & LIGHTKAILUAHI96734CENTRAL PACIFIC BANKHI01997-02-2719974812.00010NNNaN31-May-97$30,000.00$0.00P I F$0.00$30,000.00$24,000.00024000.030000.02